fix: align the documented arguments with the signatures (#190)

* `as.data.table.EnsembleFSResult()` documented a `benchmark_result`
  argument that did not exist. It is implemented now, so the task,
  learner and resampling columns can be omitted.
* The `$print()` methods documented `@param ... (ignored)` but declared
  `print = function()`, so `print(archive, digits = 3)` errored with
  `unused argument`.
* `ArchiveAsyncFSelect$new()` documented a `check_values` argument that
  the constructor does not have.
* `embedded_ensemble_fselect()` documented a default for `measure`,
  which has none.
* `AutoFSelector$print()` was missing its `@description` tag.

Author: be-marc

NEWS.md

@@ -1,6 +1,8 @@
 # mlr3fselect (development version)
 
 * fix: `ArchiveAsyncFSelect` pushed results with the removed `rush::Rush$push_results()` method.
+* fix: `as.data.table()` on an `EnsembleFSResult` accepts the documented `benchmark_result` argument now to omit the task, learner and resampling columns (#190).
+* fix: The `$print()` methods of `ArchiveBatchFSelect`, `ArchiveAsyncFSelect`, `ArchiveAsyncFSelectFrozen`, `AutoFSelector` and `FSelector` errored with `unused argument` when arguments such as `digits` were passed (#190).
 * fix: `fs("rfecv")` had the same label as `fs("rfe")`, so both were indistinguishable in `as.data.table(mlr_fselectors)`. Its manual page also instructed to construct it with `fs("rfe")` (#191).
 * fix: `AutoFSelector$train()` did not check the row ids of an instantiated inner resampling for cross-validation and reported a wrong set number for holdout (#197).
 * fix: The `mlr3fselect.svm_rfe` callback accepted support vector machines without a `type` or `kernel` setting, although only `type = "C-classification"` and `kernel = "linear"` are supported. The callback now also errors on multi-class tasks for which the importance scores are not defined (#173).

R/ArchiveAsyncFSelect.R

@@ -47,9 +47,6 @@ ArchiveAsyncFSelect = R6Class(
   public = list(
     #' @description
     #' Creates a new instance of this [R6][R6::R6Class] class.
-    #'
-    #' @param check_values (`logical(1)`)\cr
-    #'   If `TRUE` (default), feature subsets are check for validity.
     initialize = function(
       search_space,
       codomain,
@@ -123,7 +120,7 @@ ArchiveAsyncFSelect = R6Class(
     #' Printer.
     #'
     #' @param ... (ignored).
-    print = function() {
+    print = function(...) {
       cat_cli(cli_h1("{format(self)} with {.val {self$n_evals}} evaluations"))
       print(
         as.data.table(

R/ArchiveAsyncFSelectFrozen.R

@@ -83,7 +83,7 @@ ArchiveAsyncFSelectFrozen = R6Class(
     #' Printer.
     #'
     #' @param ... (ignored).
-    print = function() {
+    print = function(...) {
       cat_cli(cli_h1("{.cls {class(self)[1L]}} with {.val {self$n_evals}} evaluations"))
       print(
         as.data.table(

R/ArchiveBatchFSelect.R

@@ -155,7 +155,7 @@ ArchiveBatchFSelect = R6Class(
     #' Printer.
     #'
     #' @param ... (ignored).
-    print = function() {
+    print = function(...) {
       cat_cli(cli_h1("{.cls {class(self)[1]}}"))
       print(self$data[, setdiff(names(self$data), "uhash"), with = FALSE], digits = 2)
     },

R/AutoFSelector.R

@@ -243,9 +243,11 @@ AutoFSelector = R6Class(
       }
     },
 
+    #' @description
     #' Printer.
+    #'
     #' @param ... (ignored).
-    print = function() {
+    print = function(...) {
       msg_h = if (is.null(self$label) || is.na(self$label)) "" else paste0(": ", self$label)
       model = if (is.null(self$model)) "-" else class(self$model)[1L]
 

R/EnsembleFSResult.R

@@ -671,8 +671,9 @@ EnsembleFSResult = R6Class(
 )
 
 #' @export
-as.data.table.EnsembleFSResult = function(x, ...) {
-  x$result
+as.data.table.EnsembleFSResult = function(x, ..., benchmark_result = TRUE) {
+  assert_flag(benchmark_result)
+  if (benchmark_result) x$result else copy(get_private(x)$.result)
 }
 
 #' @export

R/FSelector.R

@@ -73,8 +73,10 @@ FSelector = R6Class(
     #' @description
     #' Print method.
     #'
+    #' @param ... (ignored).
+    #'
     #' @return (`character()`).
-    print = function() {
+    print = function(...) {
       msg_h = if (is.na(self$label)) "" else paste0(": ", self$label)
       cat_cli({
         cli_h1("{.cls {class(self)[1L]}}{msg_h}")

R/embedded_ensemble_fselect.R

@@ -30,7 +30,6 @@
 #'  Can only be [mlr3::ResamplingSubsampling] or [mlr3::ResamplingBootstrap].
 #' @param measure ([mlr3::Measure])\cr
 #'  The measure used to score each learner on the test sets generated by `init_resampling`.
-#'  If `NULL`, default measure is used.
 #' @param store_benchmark_result (`logical(1)`)\cr
 #'  Whether to store the benchmark result in [EnsembleFSResult] or not.
 #'